Method and system for frame alignment and unsupervised adaptation of acoustic models

ABSTRACT

An unsupervised adaptation method and apparatus are provided that reduce the storage and time requirements associated with adaptation. Under the invention, utterances are converted into feature vectors, which are decoded to produce a transcript and alignment unit boundaries for the utterance. Individual alignment units and the feature vectors associated with those alignment units are then provided to an alignment function, which aligns the feature vectors with the states of each alignment unit. Because the alignment is performed within alignment unit boundaries, fewer feature vectors are used and the time for alignment is reduced. After alignment, the feature vector dimensions aligned to a state are added to dimension sums that are kept for that state. After all the states in an utterance have had their sums updated, the speech signal and the alignment units are deleted. Once sufficient frames of data have been received to perform adaptive training, the acoustic model is adapted.

BACKGROUND OF THE INVENTION

[0001] The present invention relates to adaptive training for speechrecognition systems. In particular, the present invention relates tounsupervised adaptive training.

[0002] Speech recognition systems identify words in speech signals. Todo this, most speech recognition systems compare the speech signal tomodels associated with small acoustic units that form all speech. Eachcomparison generates a likelihood that a particular segment of speechcorresponds to a particular acoustic unit.

[0003] The acoustic models found in most speech recognition systems aretrained using speech signals that are developed in an environment thatis different from the environment in which the speech recognition systemis later used. In particular, the speakers, microphones, and noiselevels used during training are almost always different from thespeaker, microphone, and noise level that is present when the speechrecognition system is actually used.

[0004] It has been recognized that the differences between the trainingdata and the actual data (usually referred to as test data) used duringrecognition degrades the performance of the speech recognition system.

[0005] One technique that has been used to address the differencesbetween the training data and the test data is to adaptively change theacoustic models based on a collection of test data. Thus, a model thatis initially trained on training data is modified based on actual speechsignals generated while the speech recognition system is being used inthe field.

[0006] Two types of adaptation have been used in the past: supervisedadaptation and unsupervised adaptation. In supervised adaptation, theuser reads from a script during an enrollment session. The system thenuses the user's speech signal to adjust the models for the variousacoustic units represented in the script. Although supervised adaptationis generally considered more accurate than unsupervised adaptation, itis also very boring for the users.

[0007] In unsupervised adaptation, the system adapts the acoustic modelbased on the user's normal use of the speech recognition system. Becausethe system has no way to predict what the user will say, it does nothave an exact transcript of the speech signal. Instead, the system usesthe acoustic model to decode the speech signal and thereby form thetranscript. This decoded transcript is then used to update the model.

[0008] One major problem with unsupervised adaptation is that itrequires a significant amount of time and data. In particular, in mostprior art systems, the digital input speech signal or features derivedfrom the speech signal must be stored until there is enough speech foradaptive training. Because it is difficult to predict the length of anutterance, it is difficult to estimate the size of the digitized speechsignal. Because of this, the systems cannot accurately predict how muchstorage space will be needed to store the speech data. As a result, thesystem must be equipped to handle a full disc error message at any timeduring the speech storage stage or must reserve enough disc space sothat there is sufficient space to handle the worst case size for the.WAV files. Since it is undesirable to have applications reserving moredisc space than they absolutely need, such an overestimation of thespace needed for the digitized speech signal should be avoided.

[0009] The time required to perform the training is dominated by a stepof aligning individual frames of speech with a particular acoustic unitfound in the transcription. The time needed to perform this alignment istypically a function of the square of the number of frames that need tobe aligned. Thus, a system is needed that reduces the time needed toalign frames of speech data.

SUMMARY OF THE INVENTION

[0010] An unsupervised adaptation method and apparatus are provided thatreduce the storage and time requirements associated with adaptation.Under the invention, utterances are converted into feature vectors,which are decoded to produce a transcript and alignment unit boundariesfor the utterance. Individual alignment units and the feature vectorsassociated with those alignment units are then provided to an alignmentfunction, which aligns the feature vectors with the states of eachalignment unit. Because the alignment is performed within alignment unitboundaries, fewer feature vectors are used and the time for alignment isreduced. After alignment, the feature vector dimensions aligned to astate are added to dimension sums that are kept for that state. Afterall the states in an utterance have had their sums updated, the speechsignal and the alignment units are deleted. Once sufficient frames ofdata have been received to perform adaptive training, the acoustic modelis adapted.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011]FIG. 1 is a block diagram of a general computing environment inwhich the present invention may be practiced.

[0012]FIG. 2 is a block diagram of a general mobile computingenvironment in which the present invention may be practiced.

[0013]FIG. 3 is a block diagram of a speech recognition system under thepresent invention.

[0014]FIG. 4 is a flow diagram of a method for unsupervised adaptationunder the present invention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0015]FIG. 1 illustrates an example of a suitable computing systemenvironment 100 on which the invention may be implemented. The computingsystem environment 100 is only one example of a suitable computingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of the invention. Neither should thecomputing environment 100 be interpreted as having any dependency orrequirement relating to any one or combination of components illustratedin the exemplary operating environment 100.

[0016] The invention is operational with numerous other general purposeor special purpose computing system environments or configurations.Examples of well known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, telephony systems, distributedcomputing environments that include any of the above systems or devices,and the like.

[0017] The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

[0018] With reference to FIG. 1, an exemplary system for implementingthe invention includes a general purpose computing device in the form ofa computer 110. Components of computer 110 may include, but are notlimited to, a processing unit 120, a system memory 130, and a system bus121 that couples various system components including the system memoryto the processing unit 120. The system bus 121 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus also known as Mezzanine bus.

[0019] Computer 110 typically includes a variety of computer readablemedia. Computer readable media can be any available media that can beaccessed by computer 110 and includes both volatile and nonvolatilemedia, removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 110. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer readable media.

[0020] The system memory 130 includes computer storage media in the formof volatile and/or nonvolatile memory such as read only memory (ROM) 131and random access memory (RAM) 132. A basic input/output system 133(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 110, such as during start-up, istypically stored in ROM 131. RAM 132 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 120. By way o example, and notlimitation, FIG. 1 illustrates operating system 134, applicationprograms 135, other program modules 136, and program data 137.

[0021] The computer 110 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 1 illustrates a hard disk drive 141 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 151that reads from or writes to a removable, nonvolatile magnetic disk 152,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 156 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 141 is typically connectedto the system bus 121 through a non-removable memory interface such asinterface 140, and magnetic disk drive 151 and optical disk drive 155are typically connected to the system bus 121 by a removable memoryinterface, such as interface 150.

[0022] The drives and their associated computer storage media discussedabove and illustrated in FIG. 1, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 110. In FIG. 1, for example, hard disk drive 141 is illustratedas storing operating system 144, application programs 145, other programmodules 146, and program data 147. Note that these components can eitherbe the same as or different from operating system 134, applicationprograms 135, other program modules 136, and program data 137. Operatingsystem 144, application programs 145, other program modules 146, andprogram data 147 are given different numbers here to illustrate that, ata minimum, they are different copies.

[0023] A user may enter commands and information into the computer 110through input devices such as a keyboard 162, a microphone 163, and apointing device 161, such as a mouse, trackball or touch pad. Otherinput devices (not shown) may include a joystick, game pad, satellitedish, scanner, or the like. These and other input devices are oftenconnected to the processing unit 120 through a user input interface 160that is coupled to the system bus, but may be connected by otherinterface and bus structures, such as a parallel port, game port or auniversal serial bus (USB). A monitor 191 or other type of displaydevice is also connected to the system bus 121 via an interface, such asa video interface 190. In addition to the monitor, computers may alsoinclude other peripheral output devices such as speakers 197 and printer196, which may be connected through an output peripheral interface 190.

[0024] The computer 110 may operate in a networked environment usinglogical connections to one or more remote computers, such as a remotecomputer 180. The remote computer 180 may be a personal computer, ahand-held device, a server, a router, a network PC, a peer device orother common network node, and typically includes many or all of theelements described above relative to the computer 110. The logicalconnections depicted in FIG. 1 include a local area network (LAN) 171and a wide area network (WAN) 173, but may also include other networks.Such networking environments are commonplace in offices, enterprise-widecomputer networks, intranets and the Internet.

[0025] When used in a LAN networking environment, the computer 110 isconnected to the LAN 171 through a network interface or adapter 170.When used in a WAN networking environment, the computer 110 typicallyincludes a modem 172 or other means for establishing communications overthe WAN 173, such as the Internet. The modem 172, which may be internalor external, may be connected to the system bus 121 via the user inputinterface 160, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 110, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 1 illustrates remoteapplication programs 185 as residing on remote computer 180. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

[0026]FIG. 2 is a block diagram of a mobile device 200, which is analternative exemplary computing environment. Mobile device 200 includesa microprocessor 202, memory 204, input/output (I/O) components 206, anda communication interface 208 for communicating with remote computers orother mobile devices. In one embodiment, the afore-mentioned componentsare coupled for communication with one another over a suitable bus 210.

[0027] Memory 204 is implemented as non-volatile electronic memory suchas random access memory (RAM) with a battery back-up module (not shown)such that information stored in memory 204 is not lost when the generalpower to mobile device 200 is shut down. A portion of memory 204 ispreferably allocated as addressable memory for program execution, whileanother portion of memory 204 is preferably used for storage, such as tosimulate storage on a disk drive.

[0028] Memory 204 includes an operating system 212, application programs214 as well as an object store 216. During operation, operating system212 is preferably executed by processor 202 from memory 204. Operatingsystem 212, in one preferred embodiment, is a WINDOWS® CE brandoperating system commercially available from Microsoft Corporation.Operating system 212 is preferably designed for mobile devices, andimplements database features that can be utilized by applications 214through a set of exposed application programming interfaces and methods.The objects in object store 216 are maintained by applications 214 andoperating system 212, at least partially in response to calls to theexposed application programming interfaces and methods.

[0029] Communication interface 208 represents numerous devices andtechnologies that allow mobile device 200 to send and receiveinformation. The devices include wired and wireless modems, satellitereceivers and broadcast tuners to name a few. Mobile device 200 can alsobe directly connected to a computer to exchange data therewith. In suchcases, communication interface 208 can be an infrared transceiver or aserial or parallel communication connection, all of which are capable oftransmitting streaming information.

[0030] Input/output components 206 include a variety of input devicessuch as a touch-sensitive screen, buttons, rollers, and a microphone aswell as a variety of output devices including an audio generator, avibrating device, and a display. The devices listed above are by way ofexample and need not all be present on mobile device 200. In addition,other input/output devices may be attached to or found with mobiledevice 200 within the scope of the present invention.

[0031]FIG. 3 provides a more detailed block diagram of modules that areparticularly relevant to the present invention. In FIG. 3, an inputspeech signal is converted into an electrical signal, if necessary, by amicrophone 300. The electrical signal is then converted into a series ofdigital values by an analog-to-digital converter 302. In severalembodiments, A-to-D converter 302 samples the analog signal at 16 kHzthereby creating 32 kilobytes of speech data per second.

[0032] The digital data is provided to a frame construction unit 303,which groups the digital values into frames of values. In oneembodiment, each frame is 25 milliseconds long and begins 10milliseconds after the beginning of the previous frame.

[0033] The frames of digital data are provided to a feature extractor304, which extracts a feature from the digital signal. Examples offeature extraction modules include modules for performing LinearPredictive Coding (LPC), LPC derived cepstrum, Perceptive LinearPrediction (PLP), Auditory model feature extraction, and Mel-FrequencyCepstrum Coefficients (MFCC) feature extraction. Note that the inventionis not limited to these feature extraction modules and that othermodules may be used within the context of the present invention.

[0034] The feature extraction module produces a single multi-dimensionalfeature vector per frame. The number of dimensions or values in thefeature vector is dependent upon the type of feature extraction that isused. For example, mel-frequency cepstrum coefficient vectors generallyhave 39 dimensions. Thus, for such feature vectors, each frame isassociated with 39 values that form the feature vector.

[0035] The stream of feature vectors produced by feature extractor 304is provided to a decoder 306, which identifies a most likely sequence ofwords based on the stream of feature vectors, a lexicon 308, a languagemodel 310, and an acoustic model 312.

[0036] In most embodiments, acoustic model 312 is a Hidden Markov Modelconsisting of a set of hidden states, with one state per frame of theinput signal. Each state has an associated set of probabilitydistributions that describe the likelihood of an input feature vectormatching a particular state. In some embodiments, a mixture ofprobabilities (typically 10 Gaussian probabilities) is associated witheach state. The model also includes probabilities for transitioningbetween two neighboring model states as well as allowed transitionsbetween states for particular linguistic units. The size of thelinguistic units can be different for different embodiments of thepresent invention. For example, the linguistic units may be senones,phonemes, diphones, triphones, syllables, or even whole words.

[0037] Before adaptive training, acoustic model 312 is the same as aninitial acoustic model 314. In most embodiments, the initial acousticmodel 314 has been trained based on speech signals from a variety ofspeakers. As such, it is considered a speaker-independent model.

[0038] Lexicon 308 consists of a list of linguistic units (typicallywords or syllables) that are valid for a particular language. Decoder306 uses lexicon 308 to limit its search for possible linguistic unitsto those that are actually part of the language. The lexicon alsocontains pronunciation information (i.e. mappings from each linguisticunit to a sequence of acoustic units used by the acoustic model.

[0039] Language model 310 provides a set of likelihoods that aparticular sequence of linguistic units will appear in a particularlanguage. In many embodiments, the language model is based on a textdatabase such as the North American Business News (NAB), which isdescribed in greater detail in a publication entitled CSR-III TextLanguage Model, University of Penn., 1994. The language model may be acontext-free grammar, a statistical N-gram model such as a trigram, or acombination of both. In one embodiment, the language model is a compacttrigram model that determines the probability of a sequence of wordsbased on the combined probabilities of three-word segments of thesequence.

[0040] Based on the acoustic model, the language model, and the lexicon,decoder 306 identifies a most likely sequence of linguistic units fromall possible linguistic unit sequences. This sequence of linguisticunits represents a transcript of the speech signal. Decoder 306 alsoprovides an indication of the starting frame number and ending framenumber associated with an alignment unit. An alignment unit can be anyunit that is aligned with frames of the speech signal by the decoder. Inmost embodiments, the alignment units are the linguistic unitsidentified by the decoder. However, in other embodiments, the alignmentunits can be collections of linguistic units (phrases), phonemes, orsub-phonemes. Thus, by indicating the starting frame and ending frameassociated with each alignment unit decoder 306 indicates the alignmentbetween the frames and the alignment unit boundaries.

[0041] The transcript is provided to an output model 318, which handlesthe overhead associated with transmitting the transcript to one or moreapplications. In one embodiment, output module 318 communicates with amiddle layer that exists between the speech recognition engine of FIG. 3and one or more applications.

[0042] Under the present invention, the transcript provided by decoder306 is also used to perform unsupervised adaptation of acoustic model312. A method for performing such adaptation is described below withreference to the block diagram of FIG. 3 and a flow diagram shown inFIG. 4.

[0043] At step 400 of FIG. 4, the speech recognition system of FIG. 3waits for an utterance from a user. In this context, an utterance is aspeech signal of any length that is delimited by pauses or relativesilence. When it receives an utterance, the speech recognition systemgenerates the feature vectors associated with the speech signal anddecodes the feature vectors to produce a transcript and a set ofalignment unit boundaries as described above. This step of decoding isshown as step 402 in FIG. 4.

[0044] The transcript is provided to a trainer controller 320 of FIG. 3along with the alignment unit boundary information provided by decoder306 and the feature vectors provided by feature extractor 304. For eachalignment unit in the transcript, trainer controller 320 uses lexicon308 to identify the acoustic units (typically phonemes) that form thealignment unit. Trainer controller 320 also identifies the featurevectors associated with each alignment unit based on the alignment unitboundaries and the sequence of feature vectors. In step 404 of FIG. 4,the acoustic units and feature vectors for the first alignment unit inthe utterance are passed to an aligner 322.

[0045] At step 406, aligner 322 aligns the feature vectors with theindividual states that form the acoustic units of the alignment unit.The same state within an acoustic unit may be repeated any number oftimes. Under embodiments that use a Viterbi training method, only asingle feature vector can be assigned to a single occurrence of a state.However, a group of feature vectors may all be assigned to differentoccurrences of the same state in an acoustic unit. The states found ineach acoustic unit are provided by acoustic model 312.

[0046] In other embodiments that utilize Forward-Backward training, thealignment step assigns a fraction of each frame to a number of differentstates. The fraction of the frame that is assigned to each state isbased on the likelihood that the frame of speech is aligned with thestate.

[0047] Techniques for aligning feature vectors with states are wellknown in the art. For example, see Fundamentals of Speech Recognition,Lawrence Rabiner & Biin-Hwang Juang, Prentice Hall, 1993 (ParticularySec. 4.7, Time Alignment and Normalization).

[0048] The time needed to align the states with the feature vectors is afunction of the number of states multiplied by the number of featurevectors. Because the present invention aligns the states and featurevectors on an alignment unit-by-alignment unit basis instead of on anutterance basis, it reduces the amount of time needed to perform thealignment. The time savings can be roughly represented as:$\begin{matrix}{{O\left( {\sum\limits_{n = 1}^{N}{S_{n}*{\sum\limits_{n = 1}^{N}F_{n}}}} \right)} - {\sum\limits_{n = 1}^{N}{O\left( {S_{n}*F_{n}} \right)}}} & {{EQ}.\quad 1}\end{matrix}$

[0049] where o( ) represents an alignment time function, N is the totalnumber of alignment units in an utterance, S is the number of states inan alignment unit and F is the number of feature vectors associated withan alignment unit.

[0050] After the feature vectors have been aligned with the states, thefeature vectors are used at step 408 to update dimension sums and framecounts for each state in the alignment unit. Each state contains aseparate dimension sum for each dimension of the feature vector. Thus,if each feature vector had 39 dimensions, each state would have 39dimension sums.

[0051] Under a Viterbi training method, a dimension sum for a state isupdated by adding the value of the dimension found in each featurevector that was aligned with the state. Thus, if a state was alignedwith three feature vectors, each dimension sum in the state would havethree values added to it, one for each vector.

[0052] The frame count for a state keeps track of the number of vectorsthat have been assigned to the state. Thus, if three vectors wereassigned to a state for the current word, the frame count would beupdated by adding “3” to the count. The updated dimensions sums andframe counts are stored in a memory storage 324 in FIG. 3.

[0053] Additional statistics needed for acoustic model adaptation (suchas sum of squares of feature values) may also be collected in a similarmanner. The choice of the statistics that are collected depends upon thedetails of the chosen adaptation method.

[0054] In embodiments that use Forward-Backward training, each dimensionsum is updated by multiplying each feature vector assigned to the stateby the alignment probability for the frame/state pair and adding theproducts to the sum. Similarly, the frame count is updated by adding allof the new frame/state probabilities associated with a state.

[0055] In still further embodiments that use a mixture of Gaussians ateach state, a separate dimension sum and a separate frame count aremaintained for each mixture component. The feature vector values and theframe count values associated with a frame/state pair are thendistributed across the dimension sums and frame counts of the mixturecomponents based on how well the feature vector matches each componentGaussian.

[0056] At step 410 of FIG. 4, trainer controller 320 determines if thereare more alignment units in the current utterance. If there are morealignment units, the process of FIG. 4 returns to step 404 so that thenext alignment unit in the utterance can be aligned with its featurevectors and have its states' dimension sums updated.

[0057] If there are no more alignment units in the utterance, theprocess continues at step 412, where the transcript for the currentutterance is output by output module 318 and then deleted from thespeech recognition memory along with the feature vectors and any digitalrepresentations of the speech signal that may have been stored in therecognition system. Thus, after step 412, only the dimension sums andthe frame counts are left as indications of the utterance. Since thesesums take a fixed pre-determinable amount of disc space, they are easierto store than the actual speech signal, as was done in the prior art.

[0058] After the transcript and speech signal have been deleted, theprocess continues at step 414 where a model adapter 326 determines ifthere has been enough speech to warrant adapting the acoustic model. Inone embodiment, five minutes of speech since the last adaptation isconsidered enough to warrant performing another adaptation of the model.If more speech is desired before adaptation, the process returns to step400 to wait for the next utterance.

[0059] If there is enough speech for adaptation, model adapter 326adapts an initial acoustic model 314 using the dimension sums and framecounts from storage 324 to form a new version of acoustic model 312 atstep 416. In particular, the sums are divided by their respective framecounts to form an average value for each dimension in each state foundin the current set of utterances. These average values are then used toadaptively train the models for each dimension in each state.

[0060] Any known methods of unsupervised adaptation may be used. In oneembodiment, a combination of Maximum Likelihood Linear Regression (MLLR)and Maximum A Posteriori (MAP) adaptation are used to adapt the initialacoustic model 314. However, other adaptation techniques may be usedwithin the scope of the present invention.

[0061] In one embodiment, the dimension sums and frame counts aremaintained even after adaptation. Thus, with each adaptation iteration,these sums grow larger. Under such embodiments, the adaptation isperformed on the initial acoustic model at each iteration and is notperformed on a previously adapted acoustic model. In other embodiments,the sums are cleared after each adaptation iteration and the latestversion of the acoustic model is trained during the next adaptationiteration.

[0062] In one embodiment, the step of adapting the acoustic model isperformed on a separate thread from the thread on which the speechdecoding, frame alignment and dimension sum updating operate. Inparticular, a relatively low priority thread may be used for theadaptation. This reduces the degree to which the adaptation affects thedecoding process. Although it is operating on a separate thread, themodel adapter may update acoustic model 312 without performing a lockoutoperation on the model since the model adapter is the only module thatwrites to the acoustic model. Thus, decoder 306 may continue to use themodel parameters even while model adapter 326 is updating the model.

[0063] Although the process of using alignment unit boundaries to alignacoustic states with the frames of a speech signal has been described inconnection with unsupervised training, this aspect of the presentinvention is not limited to unsupervised training. In other embodiments,it is used as part of supervised training and in still furtherembodiments it is used in methods unrelated to training.

[0064] Although the present invention has been described with referenceto preferred embodiments, workers skilled in the art will recognize thatchanges may be made in form and detail without departing from the spiritand scope of the invention. In particular, although the modules of FIG.3 have been described as existing within closed computing environment,in other embodiments, the modules are distributed across a networkedcomputing environment.

What is claimed is:
 1. A method of storing speech information for use inretraining a speech model, the method comprising: receiving a speechsignal; identifying at least one feature value for each of a set offrames of a speech signal; decoding the speech signal based on thespeech model to identify a sequence of alignment units; aligning a stateof an alignment unit from the sequence of alignment units with a framein the set of frames of the speech signal; and before receiving enoughframes of the speech signal to begin retraining, adding at least onefeature value that is identified for a frame to a feature value sum thatis associated with the state that is aligned with the frame.
 2. Themethod of claim 1 wherein the speech signal comprises a singleutterance.
 3. The method of claim 1 wherein the steps of identifying,decoding, aligning, and adding are repeated for each of a plurality ofutterances.
 4. The method of claim 3 wherein for each utterance the stepof adding to a feature value sum comprises adding to a feature value sumgenerated from a previous utterance.
 5. The method of claim 1 furthercomprising adding to a frame count associated with a state each time afeature value is added to the feature value sum associated with thestate.
 6. The method of claim 5 further comprising retraining the speechmodel based on the feature value sums and the frame counts associatedwith the states.
 7. The method of claim 6 wherein retraining the speechmodel comprises dividing each state's feature value sum by the state'sframe count to form an average value for each state.
 8. The method ofclaim 6 wherein retraining the speech model comprises starting a newcomputing thread on which the training operations are performed.
 9. Themethod of claim 8 wherein retraining the speech model further comprisesupdating at least one speech model parameter without locking out thespeech model so that the speech model is available for decoding duringtraining.
 10. The method of claim 6 further comprising after retrainingthe speech model repeating the steps of identifying, decoding, aligning,and adding for a new utterance.
 11. The method of claim 10 whereinadding to a feature value sum for a state after retraining the speechmodel comprises adding to the feature value sum that was used to retrainthe model.
 12. The method of claim 1 wherein decoding the speech signalfurther comprises assigning frames to alignment units and whereinaligning comprises aligning the states that form the alignment unit withframes assigned to the alignment unit.
 13. The method of claim 12wherein the alignment unit is a word.
 14. The method of claim 5 whereinmultiple feature value sums and multiple frame counts are associatedwith each state.
 15. A speech recognition system for recognizinglinguistic units in a speech signal, the system comprising: an acousticmodel; a decoder that uses the acoustic model to identify alignmentunits in the speech signal; an aligner that aligns states of thealignment units identified by the decoder with frames of the speechsignal; a dimension sum storage that stores feature dimension sums thatare associated with states in the alignment units, at least one state'ssums updated before a sufficient number of frames of the speech signalare available to train the acoustic model, each state's sums updated bysumming dimension values from feature vectors assigned to frames alignedwith the state; and a model adapter that uses the feature dimension sumsto train the acoustic model after a sufficient number of frames of thespeech signal are available.
 16. The speech recognition system of claim15 further comprising a trainer controller that causes the frames of thespeech signal to be deleted after the feature dimension sums are formedbut before the model adapter trains the acoustic model.
 17. The speechrecognition system of claim 15 further comprising an initial acousticmodel, wherein the model adapter trains the acoustic model by adaptingthe parameters of the initial acoustic model to form a new version ofthe acoustic model.
 18. The speech recognition system of claim 15wherein the model adapter is a set of computer-executable instructionsthat are processed on a different thread from the decoder.
 19. Thespeech recognition system of claim 15 wherein the decoder assigns framesof the speech signal to words and wherein the aligner aligns the framesassigned to a word with the states of the word.
 20. A method of aligningframes of a speech signal to states for a sequence of linguistic units,the method comprising: identifying alignment units corresponding to thesequence of linguistic units and identifying a set of frames that areassociated with each alignment unit; for each alignment unit in thesequence of alignment units: identifying the states associated with thealignment unit; and aligning the set of frames associated with thealignment unit by the decoder with the states associated with thealignment unit.
 21. The method of claim 20 wherein the method is part ofa process of associating feature vectors that represent the speechsignal with states of words.
 22. The method of claim 21 wherein there isone feature vector per frame and each feature vector comprises aplurality of dimensions.
 23. The method of claim 22 wherein the methodis used in a process of adapting an acoustic model that furthercomprises generating a set of dimension sums for each state, eachdimension sum being associated with a different dimension of the featurevectors, a dimension sum being formed by summing at least a portion ofthe values of a respective dimension from all of the feature vectorsassociated with a state.
 24. The method of claim 23 wherein the processof adapting an acoustic model further comprises using the dimension sumsto adapt the acoustic model.
 25. The method of claim 24 wherein theprocess of adapting an acoustic model further comprises using thedimension sums to change the parameters of an initial acoustic model toform an adapted acoustic model.
 26. A frame alignment system foraligning frames of speech with acoustic states found in alignment units,the alignment system comprising: a decoder that identifies a sequence ofalignment units from a speech signal and associates respective sets offrames of the speech signal with alignment units in the sequence ofalignment units; a trainer controller that identifies acoustic statesfor the alignment units in the sequence of alignment units; and analigner that aligns the acoustic states of an alignment unit with framesin the set of frames associated with the alignment unit.
 27. The framealignment system of claim 26 further comprising an acoustic model thatis used by the decoder to identify the sequence of alignment units fromthe speech signal.
 28. The frame alignment system of claim 27 whereinthe frame alignment system forms part of a model adaptation system foradapting the acoustic model.
 29. The frame alignment system of claim 28wherein the model adaptation system further comprises a featureextractor that generates a feature vector for each frame of the speechsignal, each feature vector comprising a plurality of dimension valuesfor respective dimensions of the feature vector.
 30. The frame alignmentsystem of claim 29 wherein the model adaptation system further comprisesa dimension sum storage for storing a plurality of dimension sums foreach state, each dimension sum being associated with a dimension of thefeature vectors and being formed by adding the dimension values for thatdimension that are found in the feature vectors that are associated withframes aligned with the state.
 31. The frame alignment system of claim30 wherein the model adaptation system further comprises a model adapterthat uses the dimension sums to adapt the acoustic model.